Remote sensing dataset for machine learning in archaeological site recognition

The repository's data contains fragments of imagery used as input for machine learning. This data was developed from three types of imagery: Aerial photographs, Rao's Q index created from a DTM, SAR imagery. All three source layers were converted to grayscale images and then combined in specific proportions: 65% aerial photographs, 20% Rao's Q index, and 15% SAR imagery. These chosen proportions maximized the model's accuracy by mitigating noise in the SAR data while still incorporating subtle terrain variations. The conversion to grayscale was done to facilitate faster model training and to account for the varying colors of vegetation and soil features. The current dataset update includes the full weight matrix (weights) obtained from the machine learning process of the Mask R-CNN model. This file records the network state after completing a full cycle of 250 training epochs, implemented in the TensorFlow environment. Users can initialize the model architecture with these weights, allowing for immediate mask generation and archaeological anomaly detection on new RGB/SAR data without having to repeat the computationally expensive training process. The provided weights can serve as a starting point (pre-trained weights) for other researchers who would like to adapt the model to local specificities in other geographic regions, significantly accelerating the convergence of new models. Including the weights enables a thorough analysis of how the network interprets the fusion of Rao's Q and SAR intensity, which is essential for a reliable assessment of detection reliability in remote sensing archaeology. This research was funded by the National Science Centre, Poland under Grant no. 2024/08/X/ST10/00587本仓库所包含的数据为用于机器学习任务的图像片段。该数据集源自三类图像源：航空摄影图像、由数字地形模型（DTM）生成的Rao Q指数（Rao's Q index）、合成孔径雷达（SAR）图像。 三类源图层均被转换为灰度图像，并按照特定比例融合：航空摄影图像占65%、Rao Q指数占20%、SAR图像占15%。该比例配置通过抑制SAR数据中的噪声，同时保留细微地形变化，实现了模型准确率的最大化。转换为灰度图像是为了加快模型训练速度，并适配植被与土壤特征的色彩差异。 本次数据集更新包含了Mask R-CNN模型机器学习流程中得到的完整权重矩阵（weights）。该文件记录了在TensorFlow环境下完成250个训练周期全流程后的网络状态。研究人员可通过该权重初始化模型架构，无需重复计算量巨大的训练过程，即可直接对新的RGB/SAR数据进行掩膜生成与考古异常检测。所提供的权重可作为预训练权重（pre-trained weights），供其他研究者将模型适配至其他地理区域的本地化场景，大幅加速新模型的收敛速度。引入该权重矩阵还可用于深入分析网络如何解读Rao Q指数与SAR强度的融合特征，这对于遥感考古中检测可靠性的可靠评估至关重要。 本研究由波兰国家科学中心资助，项目编号为2024/08/X/ST10/00587。